{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfig = plt.figure()\\nlinex = np.arange(1, 10)\\nliney = np.arange(1, 10)\\nsubplot = fig.add_subplot(1, 2, 1)\\nsubplot.scatter(linex, liney)\\nsubplot = fig.add_subplot(1,2 , 2)\\nsubplot.plot(linex, liney)\\n\\nplt.show()\\n'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "fig = plt.figure()\n",
    "linex = np.arange(1, 10)\n",
    "liney = np.arange(1, 10)\n",
    "subplot = fig.add_subplot(1, 2, 1)\n",
    "subplot.scatter(linex, liney)\n",
    "subplot = fig.add_subplot(1,2 , 2)\n",
    "subplot.plot(linex, liney)\n",
    "\n",
    "plt.show()\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_x = tf.placeholder(tf.float32, shape=[None,1])\n",
    "input_y = tf.placeholder(tf.float32, shape=[None,1])\n",
    "\n",
    "weights = tf.Variable(tf.random_normal([1,1]))\n",
    "bias = tf.Variable(tf.random_normal([1]))\n",
    "\n",
    "h = input_x*weights+bias\n",
    "\n",
    "cost = tf.reduce_mean(tf.square(h - input_y))\n",
    "\n",
    "train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-28-d73f309817c0>:3: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n"
     ]
    }
   ],
   "source": [
    "global result\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.initialize_all_variables())\n",
    "    x = [[10],[9],[3],[2]]\n",
    "    y = [[90],[80],[50],[30]]\n",
    "    for i in range(2000):\n",
    "        sess.run(train, feed_dict={input_x:x, input_y:y})\n",
    "    result = sess.run(h, feed_dict={input_x:x, input_y:y})\n",
    "    weights_variable = sess.run(weights)\n",
    "    bias_variable = sess.run(bias)\n",
    "    #print(sess.run(h, feed_dict={input_x:[50]}))\n",
    "    \n",
    "    #print(weights_variable*50+bias_variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "공부한 시간을 입력해주세요50\n",
      "예상성적은[[ 352.90423584]]입니다.\n"
     ]
    }
   ],
   "source": [
    "value = input(\"공부한 시간을 입력해주세요\")\n",
    "print(\"예상성적은\" + str(weights_variable*float(value)+bias_variable)+\"입니다.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TdRfRZIqlQe+6YIi2KbNUxu6hzykoZtihnWnbStfOSpOc7O4bzawH8IqZraj5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20d8cbce208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linex= np.array(x)\n",
    "linex= linex.reshape(-1)\n",
    "\n",
    "liney = np.array(y)\n",
    "liney = liney.reshape(-1)\n",
    "\n",
    "predictY= np.array(result)\n",
    "predictY = predictY.reshape(-1)\n",
    "\n",
    "fig = plt.figure()\n",
    "subplot =fig.add_subplot(1,2,1)\n",
    "subplot.plot(linex, liney)\n",
    "subplot.set_title(\"REAL\")\n",
    "\n",
    "subplot = fig.add_subplot(1,2,2)\n",
    "subplot.plot(linex, predictY)\n",
    "subplot.set_title(\"PREDICT\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}